Package: ondisc
Title: Fast, Universal, and Intuitive Computing on Large-Scale
        Single-Cell Data
Version: 1.0.0
Authors@R: 
    c(person(given = "Timothy",
           family = "Barry",
           role = c("aut", "cre"),
           email = "tbarry2@andrew.cmu.edu",
           comment = c(ORCID = "0000-0002-4356-627X")), 
           person(given = "Eugene",
           family = "Katsevich",
           role = "ths",
           comment = c(ORCID = "0000-0003-0598-2050")),
           person(given = "Kathryn",
           family = "Roeder",
           role = "ths"))
Description: Single-cell datasets are growing in size, posing challenges as well as opportunities for biology researchers. 'ondisc' (short for "on-disk single cell") enables users to easily and efficiently analyze large-scale single-cell data. 'ondisc' makes computing on large-scale single-cell data FUN: Fast, Universal, and iNtuitive.
License: MIT + file LICENSE
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1
URL: https://timothy-barry.github.io/ondisc/
Suggests: testthat, knitr, rmarkdown, covr
Imports: readr, methods, magrittr, rhdf5, data.table, Matrix, Rcpp,
        crayon, dplyr
Depends: R (>= 3.5.0)
VignetteBuilder: knitr
LinkingTo: Rcpp, Rhdf5lib
SystemRequirements: GNU make
NeedsCompilation: yes
Packaged: 2021-03-04 03:57:43 UTC; timbarry
Author: Timothy Barry [aut, cre] (<https://orcid.org/0000-0002-4356-627X>),
  Eugene Katsevich [ths] (<https://orcid.org/0000-0003-0598-2050>),
  Kathryn Roeder [ths]
Maintainer: Timothy Barry <tbarry2@andrew.cmu.edu>
Repository: CRAN
Date/Publication: 2021-03-05 09:10:02 UTC
Built: R 4.2.3; x86_64-w64-mingw32; 2024-04-24 02:47:03 UTC; windows
ExperimentalWindowsRuntime: ucrt
Archs: x64
